混合蛛猴算法及其在乙炔加氢参数优化中的应用

Hybrid Spider Monkey Optimization Algorithm and Its Application in Optimization of Acetylene Hydrogenation Parameters

蛛猴算法(SMO)是模拟蛛猴觅食行为的群智能优化算法,因其具有其良好的自组织能力而被广泛应用于数值优化领域。本文提出了一种混合蛛猴算法(QSMO)。该算法在SMO的基础上引入Metropolis准则、二次逼近法、局部随机搜索策略,并结合人工蜂群算法提高种群多样性,有效地提升了算法性能。选取了多个标准测试函数进行仿真对比,结果表明SMO的搜索精度与搜索速度均得到了显著提升。基于混合优化算法进行工业乙炔加氢反应器模型参数优化,结果表明该算法能够更好地求解工程优化问题。

The spider monkey optimization algorithm (SMO) is a swarm intelligence optimization algorithm that can simulate foraging behavior of spider monkey,and has been widely applied in numerical optimization due to its good self-organizing ability.In this paper,a hybrid spider monkey optimization algorithm (QSMO) is proposed,which integrates Metropolis criterion,quadratic approximation method,and local random search strategy with artificial bee colony algorithm to improve the population diversity.A number of standard test functions are selected to verify the effectiveness of the propose QSMO algorithm.By comparing the simulation results,it is shown that the search accuracy and search speed of SMO are significantly improved.Finally,the improved optimization algorithm is utilized to achieve the optimization of the parameters of the industrial acetylene hydrogenation reactor model.